AI-Enhanced Facility Finder
Description
Many facilities are required to register for industrial stormwater permits but do not do so. To find unpermitted facilities, EPA personnel use approaches that are time-consuming, inefficient, and infeasible to scale to the millions of facilities in the U.S.
Detailed example
The model outputs the geolocation of facilities at increased likelihood of needing an industrial stormwater permit but lacking one. It also outputs a risk score, from “No risk” to “High Risk” for permitted facilities, which is used as one of many inputs by human reviewers.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
AI-Enhanced Facility Finder saves time for EPA employees by identifying facilities most likely to lack necessary industrial stormwater permits, which are then reviewed by humans. Testing at the state level has shown these identifications increase the number of facilities that are permitted and monitored, resulting in cleaner water for Americans.
Audit / financial statement impact
The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
Controls / human review
ATO: No; PIA: Not published
Data needed
Data from the following datasets were utilized in conjunction with EPA permitting data and the EPA-derived Address Comparison Tool (ACT) to score the likelihood of a facility not having an industrial stormwater permit. The model was trained by utilizing previous targeting efforts by technical staff before the development of the model and was tested with various trial runs before releasing as a pilot to test its effectiveness in practice. - EPA and state permitting data (e.g., address, permit status). - Facility information via paid subscription with Dunn & Bradstreet (e.g., number of employees, facility size, revenue, corporate hierarchy, industrial sector, and credit risk indicators). - Employment data from the Longitudinal Employer-Household Dynamics (LODES). - Socioeconomic indicators from the American Community Survey data (ACS, 2017-2021). - Distance to waters by using the National Wetlands Inventory. - Elevation from the USGS Elevation Point Query Service (EPQS) - Aerial satellite imagery